library(tidyverse)

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library(knitr)
library(scales)

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library(plotly)

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# Source helper functions
source("../scripts/02_data_processing.R")

# Load all data
data <- load_all_data()

# Color palette
hawaii_colors <- c(
  ocean = "#006BA6",
  lava = "#C1121F",
  forest = "#2D6A4F",
  sand = "#F4A261",
  sunset = "#E76F51",
  sky = "#219EBC"
)

Overview

Hawaii’s Big Island

Largest Hawaiian Island
4,028 square miles
206,400 residents (2023)
Active volcanic zones
Diverse microclimates

Analysis Focus

Demographics
Climate
Economics
Natural Disasters

Executive Summary

Population: Growing 4.9% since 2015, but aging rapidly
Economy: Tourism-dependent with $70,200 median income
Climate: Warming trends, extreme heat days tripled
Hazards: $800M in volcanic damage (2018), ongoing tsunami risk

Demographics

Population Growth

p <- ggplot(data$demographics, aes(x = year, y = population)) +
  geom_line(color = hawaii_colors["ocean"], size = 2) +
  geom_point(color = hawaii_colors["ocean"], size = 4) +
  scale_y_continuous(labels = comma, limits = c(190000, 210000)) +
  labs(
    title = "Steady Population Growth",
    subtitle = "196,823 (2015) → 206,400 (2023)",
    x = NULL,
    y = "Population"
  ) +
  theme_minimal(base_size = 16) +
  theme(plot.title = element_text(face = "bold", size = 20))

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
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## generated.

ggplotly(p) %>%
  layout(hovermode = "x unified")

Aging Population

aging_data <- data$demographics %>%
  select(year, percent_65_over, percent_under_18) %>%
  pivot_longer(cols = c(percent_65_over, percent_under_18),
               names_to = "group", values_to = "percentage") %>%
  mutate(group = ifelse(group == "percent_65_over", "65 and Over", "Under 18"))

ggplot(aging_data, aes(x = year, y = percentage, color = group)) +
  geom_line(size = 1.5) +
  geom_point(size = 3) +
  scale_color_manual(values = c("65 and Over" = hawaii_colors["lava"],
                                 "Under 18" = hawaii_colors["ocean"])) +
  labs(
    title = "Age Distribution Shift",
    x = NULL,
    y = "Percentage",
    color = "Age Group"
  ) +
  theme_minimal(base_size = 14) +
  theme(legend.position = "bottom")

## Warning: No shared levels found between `names(values)` of the manual scale and the
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Key Trends

Median age: 38.2 → 40.5
Seniors (65+): 17.2% → 21.1%
Youth (<18): 21.5% → 19.3%

Implications

Healthcare demands ↑
School enrollment ↓
Workforce challenges

Cultural Diversity

ggplot(data$race_composition, aes(x = reorder(category, percentage), y = percentage)) +
  geom_col(fill = hawaii_colors["sunset"], width = 0.7) +
  geom_text(aes(label = paste0(percentage, "%")),
            hjust = -0.2, size = 6, fontface = "bold") +
  coord_flip() +
  scale_y_continuous(limits = c(0, 40), expand = c(0, 0)) +
  labs(
    title = "No Single Majority - Truly Multicultural",
    x = NULL,
    y = "Percentage of Population"
  ) +
  theme_minimal(base_size = 16) +
  theme(plot.title = element_text(face = "bold", size = 18))

Climate

Monthly Patterns

climate_data <- data$climate_monthly %>%
  mutate(month = factor(month, levels = month.name))

ggplot(climate_data) +
  geom_col(aes(x = month, y = avg_precip_in),
           fill = hawaii_colors["ocean"], alpha = 0.6, width = 0.7) +
  geom_line(aes(x = month, y = avg_temp_f / 5, group = 1),
            color = hawaii_colors["lava"], size = 1.5) +
  geom_point(aes(x = month, y = avg_temp_f / 5),
             color = hawaii_colors["lava"], size = 4) +
  scale_y_continuous(
    name = "Precipitation (inches)",
    sec.axis = sec_axis(~ . * 5, name = "Temperature (°F)")
  ) +
  labs(
    title = "Hilo Climate: Stable Temperature, Variable Rainfall",
    x = NULL
  ) +
  theme_minimal(base_size = 14) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
    plot.title = element_text(face = "bold", size = 17),
    axis.title.y.left = element_text(color = hawaii_colors["ocean"], size = 14),
    axis.title.y.right = element_text(color = hawaii_colors["lava"], size = 14)
  )

Climate Change Signals

ggplot(data$climate_annual, aes(x = year)) +
  geom_line(aes(y = avg_temp_f), color = hawaii_colors["lava"], size = 1.8) +
  geom_point(aes(y = avg_temp_f), color = hawaii_colors["lava"], size = 4) +
  geom_col(aes(y = extreme_heat_days / 2 + 70),
           fill = hawaii_colors["sunset"], alpha = 0.5) +
  scale_y_continuous(
    name = "Average Temperature (°F)",
    sec.axis = sec_axis(~ (. - 70) * 2, name = "Extreme Heat Days")
  ) +
  labs(
    title = "Warming Trend: Extreme Heat Days Tripled",
    subtitle = "12 days (2015) → 38 days (2023)",
    x = "Year"
  ) +
  theme_minimal(base_size = 14) +
  theme(plot.title = element_text(face = "bold", size = 18))

Temperature increased 2.3°F from 2015 to 2023. Extreme heat days (above 90°F) more than tripled.

Economics

Income Growth

p2 <- ggplot(data$economics, aes(x = year, y = median_income)) +
  geom_line(color = hawaii_colors["forest"], size = 2) +
  geom_point(color = hawaii_colors["forest"], size = 4) +
  scale_y_continuous(labels = dollar, limits = c(50000, 75000)) +
  labs(
    title = "Median Income: 29.5% Growth",
    subtitle = "$54,200 → $70,200",
    x = NULL,
    y = "Median Household Income"
  ) +
  theme_minimal(base_size = 16) +
  theme(plot.title = element_text(face = "bold", size = 20))

ggplotly(p2) %>%
  layout(hovermode = "x unified")

Unemployment Recovery

ggplot(data$economics, aes(x = year, y = unemployment_rate)) +
  geom_area(fill = hawaii_colors["lava"], alpha = 0.3) +
  geom_line(color = hawaii_colors["lava"], size = 2) +
  geom_point(color = hawaii_colors["lava"], size = 4) +
  geom_hline(yintercept = 4.0, linetype = "dashed", color = "gray30", size = 1) +
  annotate("rect", xmin = 2019.5, xmax = 2020.5, ymin = 0, ymax = 10,
           alpha = 0.15, fill = "red") +
  annotate("text", x = 2020, y = 9.5, label = "COVID-19",
           size = 6, fontface = "bold") +
  scale_y_continuous(labels = percent_format(scale = 1), limits = c(0, 10)) +
  labs(
    title = "Unemployment: COVID Spike, Strong Recovery",
    x = "Year",
    y = "Unemployment Rate (%)"
  ) +
  theme_minimal(base_size = 16) +
  theme(plot.title = element_text(face = "bold", size = 19))

Industry Breakdown

industry_plot <- data$industry_composition %>%
  arrange(desc(employment_share)) %>%
  slice(1:5)

ggplot(industry_plot, aes(x = reorder(sector, employment_share),
                          y = employment_share)) +
  geom_col(fill = hawaii_colors["ocean"], width = 0.7) +
  geom_text(aes(label = paste0(employment_share, "%")),
            hjust = -0.2, size = 5, fontface = "bold") +
  coord_flip() +
  scale_y_continuous(limits = c(0, 35)) +
  labs(
    title = "Top 5 Sectors",
    x = NULL,
    y = "Employment Share (%)"
  ) +
  theme_minimal(base_size = 14) +
  theme(plot.title = element_text(face = "bold"))

Tourism Dominance

28.5% of jobs
Average wage: $42,000
Vulnerability to external shocks

Higher-Paying Sectors

Healthcare: $62,000
Government: $58,000
Construction: $52,000

Natural Disasters

Disaster Impact

ggplot(data$disasters, aes(x = year, y = damage_millions, fill = type)) +
  geom_col() +
  scale_y_continuous(labels = dollar_format(suffix = "M"), limits = c(0, 850)) +
  scale_fill_brewer(palette = "Set1") +
  labs(
    title = "Economic Impact of Natural Disasters",
    subtitle = "2018 Kilauea Eruption: $800M damage, 2,500 evacuations",
    x = "Year",
    y = "Damage (Millions USD)",
    fill = "Disaster Type"
  ) +
  theme_minimal(base_size = 15) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(face = "bold", size = 18)
  )

## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_col()`).

Volcanic Activity

ggplot(data$volcanic_activity, aes(x = year)) +
  geom_col(aes(y = eruption_days), fill = hawaii_colors["lava"], alpha = 0.7) +
  geom_line(aes(y = earthquakes_m3plus / 5),
            color = hawaii_colors["ocean"], size = 1.5) +
  geom_point(aes(y = earthquakes_m3plus / 5),
             color = hawaii_colors["ocean"], size = 4) +
  scale_y_continuous(
    name = "Eruption Days per Year",
    sec.axis = sec_axis(~ . * 5, name = "Earthquakes (M3+)")
  ) +
  labs(
    title = "Kilauea Activity: Eruptions and Seismic Events",
    x = "Year"
  ) +
  theme_minimal(base_size = 15) +
  theme(plot.title = element_text(face = "bold", size = 17))

2018 saw peak activity with 214 eruption days and 2,345 earthquakes M3+

Tsunami Risk

Population at Risk

Zone	Population
Evacuation Zone	12,000
High Risk	35,000
Medium Risk	58,000
Low Risk	101,400

Total High/Evac: 47,000 (23% of population)

Risk Factors

Pacific Ring of Fire
Low-lying coastal areas
Limited evacuation routes
24/7 warning system active

Recent Events

2011: Japan tsunami
Regular warning drills
Updated evacuation maps

Key Findings

Synthesis

Demographic Challenge - Aging population (21% seniors) → healthcare pressures - Declining youth population → workforce concerns

Economic Vulnerability - Tourism dependence (28.5%) → shock sensitivity - Strong recovery post-COVID → resilience

Climate Concerns - Clear warming trend (+2.3°F) - Extreme heat days tripled → adaptation needed

Persistent Hazards - Active volcanoes ($800M+ damages) - 47,000 in tsunami risk zones

Strategic Priorities

Short Term

Disaster preparedness
Climate adaptation
Tourism recovery support
Healthcare capacity

Long Term

Economic diversification
Sustainable development
Infrastructure resilience
Demographic planning

Conclusions

Hawaii’s Big Island stands at a crossroads

Strengths

Economic recovery
Cultural diversity
Natural beauty
Tourism appeal

Challenges

Climate change
Natural hazards
Aging population
Economic concentration

Sustainable planning is essential for the island’s future prosperity and resilience

Questions?

Phoenix Stout

MA615 Final Project

December 15, 2025

GitHub Repository

Hawaii’s Big Island

Key Insights and Trends

Phoenix Stout

December 15, 2025

Overview

Hawaii’s Big Island

Analysis Focus

Executive Summary

Demographics

Population Growth

Aging Population

Key Trends

Implications

Cultural Diversity

Climate

Monthly Patterns

Climate Change Signals

Economics

Income Growth

Unemployment Recovery

Industry Breakdown

Tourism Dominance

Higher-Paying Sectors

Natural Disasters

Disaster Impact

Volcanic Activity

Tsunami Risk

Population at Risk

Risk Factors

Recent Events

Key Findings

Synthesis

Strategic Priorities

Short Term

Long Term

Conclusions

Strengths

Challenges